CN111780817A - Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter - Google Patents
Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter Download PDFInfo
- Publication number
- CN111780817A CN111780817A CN202010516882.2A CN202010516882A CN111780817A CN 111780817 A CN111780817 A CN 111780817A CN 202010516882 A CN202010516882 A CN 202010516882A CN 111780817 A CN111780817 A CN 111780817A
- Authority
- CN
- China
- Prior art keywords
- signal
- low
- frequency excitation
- noise
- processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/56—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using electric or magnetic effects
- G01F1/58—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by using electric or magnetic effects by electromagnetic flowmeters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Abstract
The invention discloses an algorithm for detecting and processing a noise signal of a low-frequency excitation electromagnetic flowmeter, which comprises the following steps: s1: collecting and storing signals of the low-frequency excitation electromagnetic flowmeter; s2: processing signals of the low-frequency excitation electromagnetic flowmeter; s3: and carrying out noise reduction analysis on the low-frequency excitation noise signal. The algorithm is mainly applied to signal processing of industrial field electromagnetic flow meters. The algorithm is fully suitable for the field situation, and after the acquired signals are processed, the beneficial effect of improving the measurement precision of the electromagnetic flowmeter can be achieved.
Description
Technical Field
The invention relates to the technical field of signal processing of electromagnetic flowmeters, in particular to a noise signal detection and processing algorithm of a low-frequency excitation electromagnetic flowmeter.
Background
The flowmeter is an instrument for measuring the flow of pipeline fluid by utilizing a physical principle, the electromagnetic flowmeter is novel flow measuring equipment based on a Faraday's law of electromagnetic induction, has the advantages of simple structure, wide range, corrosion resistance and the like, is widely applied to dredging engineering of ports, urban sewage treatment engineering and industrial fields of petroleum, chemical engineering, metallurgy, papermaking and the like, and is easy to cause unstable measurement due to various interferences due to complex working field conditions, thereby causing great influence on related process control. Common interferences are: differential interference, series-mode interference, common-mode interference, direct-current noise, etc., so that the output signal thereof cannot accurately reflect the flow value, and how to effectively suppress the noise signal contained in the measurement signal becomes a problem to be solved urgently. At present, the electromagnetic flow meters mostly adopt products of foreign or joint-funded enterprises, such as cologne, river, E + H, ABB and the like, and the electromagnetic flow meters produced by the enterprises occupy a large domestic market share due to high measurement precision, stable performance and small fluctuation of measured slurry flow, but specific technical details are not published. In order to realize independent and independent domestic related technologies, the development of a set of signal processing technology of the electromagnetic flowmeter is significant.
At present, the method for improving the excitation frequency is mainly adopted abroad to improve the signal-to-noise ratio and the measurement precision of the flow signal of the electromagnetic flowmeter, and the low-frequency excitation method is mainly adopted domestically, has the characteristics of inhibiting series mode interference, orthogonal interference and in-phase interference, and has better zero point stability. However, "slurry noise" occurring in low-frequency excitation seriously affects the measurement accuracy, and the difficulty in removing noise interference is extremely high.
Therefore, the noise signal detection and processing algorithm for the low-frequency excitation electromagnetic flowmeter suppresses the interference noise signal in the measuring process of the electromagnetic flowmeter, improves the measuring precision, has very important practical significance, is a technical problem to be solved urgently by technical personnel in the field at present, and has a great application prospect for domestic electromagnetic flowmeter production enterprises.
Disclosure of Invention
In view of this, the invention provides a noise signal detection and processing algorithm for a low-frequency excitation electromagnetic flowmeter, which solves the problems existing in the prior art, and the specific scheme is as follows:
an algorithm for detecting and processing a noise signal of a low-frequency excitation electromagnetic flowmeter is characterized by comprising the following steps of:
s1: collecting and storing signals of the low-frequency excitation electromagnetic flowmeter;
s2: processing signals of the low-frequency excitation electromagnetic flowmeter;
s2-1: calculating the standard deviation of the low-frequency excitation signal;
s2-2: setting a noise threshold value and a filtering factor according to the standard deviation calculated by the S2-1;
s2-3: calculating the slope of the low-frequency excitation signal;
and S2-4, classifying the signals of the low-frequency excitation electromagnetic flowmeter according to the noise threshold, the filtering factor and the low-frequency excitation signal slope obtained in S2-2 and S2-3, and setting the standard of normal fluctuation signals and pulse noise signals.
S3: carrying out noise reduction analysis on the low-frequency excitation noise signal;
s3-1: judging whether the low-frequency excitation electromagnetic flowmeter signal is a normal fluctuation signal or not according to the classification in 2-4, and if so, performing fitting smoothing processing based on model data;
s3-2: in S3-1, if the determination result is negative, it is continuously determined whether the signal is an impulse noise signal, and if not, fitting smoothing processing based on the model data is performed;
s3-3: in S3-2, if the determination result is yes, filtering processing is performed;
s3-4: filtering processing based on mathematical morphology is carried out;
s3-5: the low-frequency excitation flowmeter signal based on the mathematical morphology filtering processing at S3-4 continues to be subjected to fitting smoothing processing based on discrete data;
s3-6: and performing fitting smoothing based on model data on the low-frequency excitation flow meter signal on the basis of S3-5.
Specifically, the calculation method of the Standard deviation of the low-frequency excitation signal in step S2-1 is to perform Standard deviation calculation once for every low-frequency excitation signal that collects 10 data points, and calculate a Standard deviation value for every three points, so that 10 data points obtain 8 Standard deviation values, Standard _ error _1(1,2,3), Standard _ error _2(2,3,4).. the.. Standard _ error _8(8,9,10), and sum the 8 Standard deviation values to obtain the Standard deviation value of the 10 data points.
Further, the noise threshold and the filtering factor in the step S2-2 are set according to the following criteria,
it is quantized into five section sections section _1 according to the standard deviation value obtained in the S2-1: 0 to 0.3; section _2: 0.3-1.0; section _3: 1.0-2.0; section _4: 2.0-6.0; section _5: and >6.0, and respectively setting the filtering factor and the noise threshold value as follows: section _1:40, 0.02; section _2:20, 0.03; section _3:30, 0.04; section _4:50, 0.05; section _5:100,0.0001.
Further, in step S2-3, the slope of the low-frequency excitation signal is calculated using 20-point data, and the calculation formula is Slop ═ (data20-data 0)/20.
Further, the standard of the normal fluctuation signal and the impulse noise signal in the step S2-4 is: and when the slope is sequentially increased or decreased for not less than 8 times, judging as a normal fluctuation signal, otherwise, judging and identifying the pulse noise signal.
Specifically, the criteria of the impulse noise signal are: and when the number of data with the numerical difference of the adjacent low-frequency excitation signals larger than 5 times of the noise threshold is not larger than 3 and the number of the noise slope values larger than the ratio of the noise threshold to the filter factor is not larger than 3, determining that the pulse noise signals are generated.
Specifically, the basic operations based on the filtering processing of mathematical morphology include decaying candle division, Dilation, Opening operation and Closing operation, the input signal is f (n), the structural elements are g (n),
(n) the mathematical formula for the corrosion operation of g (n) is as follows:
(n) the mathematical formula for the expansion operation of g (n) is as follows:
f (n) the mathematical formulas of the form opening and closing operations of g (n) are respectively:
specifically, the fitting smoothing process based on the discrete data is performed by using a polynomial fitting algorithm.
Specifically, in the fitting smoothing processing based on the model data, whether the signal to be output fluctuates or not and whether the curve type of the signal to be output is linear or not are judged according to a noise threshold, if the curve type is linear, the unfiltered signal is linearly output, and the filtered signal is subjected to filtering compensation calculation according to the type of a noise interval; if the noise interval is not linear, the noise interval is judged to be a curve, linear output is adopted when the filtering threshold is not more than 20 times, and otherwise, filtering compensation calculation is carried out according to the noise interval.
The invention provides a noise signal detection and processing algorithm of a low-frequency excitation electromagnetic flowmeter, which is mainly applied to signal processing of an industrial field electromagnetic flowmeter. The algorithm is fully suitable for the field situation, and after the acquired signals are processed, the beneficial effect of improving the measurement precision of the electromagnetic flowmeter can be achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an algorithm according to the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described below with reference to the flow chart of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to a flow chart shown in fig. 1, the invention provides an algorithm for detecting and processing a noise signal of a low-frequency excitation electromagnetic flowmeter, which specifically comprises the following steps:
s1: collecting and storing signals of the low-frequency excitation electromagnetic flowmeter;
s2: processing signals of the low-frequency excitation electromagnetic flowmeter;
s2-1: calculating the standard deviation of the low-frequency excitation signal;
s2-2: setting a noise threshold value and a filtering factor according to the standard deviation calculated by the S2-1;
s2-3: calculating the slope of the low-frequency excitation signal;
and S2-4, classifying the signals of the low-frequency excitation electromagnetic flowmeter according to the noise threshold, the filtering factor and the low-frequency excitation signal slope obtained in S2-2 and S2-3, and setting the standard of normal fluctuation signals and pulse noise signals.
S3: carrying out noise reduction analysis on the low-frequency excitation noise signal;
s3-1: judging whether the low-frequency excitation electromagnetic flowmeter signal is a normal fluctuation signal or not according to the classification in 2-4, and if so, performing fitting smoothing processing based on model data;
s3-2: in S3-1, if the determination result is negative, it is continuously determined whether the signal is an impulse noise signal, and if not, fitting smoothing processing based on the model data is performed;
s3-3: in S3-2, if the determination result is yes, filtering processing is performed;
s3-4: filtering processing based on mathematical morphology is carried out;
s3-5: the low-frequency excitation flowmeter signal based on the mathematical morphology filtering processing at S3-4 continues to be subjected to fitting smoothing processing based on discrete data;
s3-6: and performing fitting smoothing based on model data on the low-frequency excitation flow meter signal on the basis of S3-5.
Specifically, the calculation method of the Standard deviation of the low-frequency excitation signal in step S2-1 is to perform Standard deviation calculation once for every low-frequency excitation signal that collects 10 data points, and calculate a Standard deviation value for every three points, so that 10 data points obtain 8 Standard deviation values, Standard _ error _1(1,2,3), Standard _ error _2(2,3,4).. the.. Standard _ error _8(8,9,10), and sum the 8 Standard deviation values to obtain the Standard deviation value of the 10 data points.
Further, the noise threshold and the filtering factor in the step S2-2 are set according to the following criteria,
quantizing the standard deviation value obtained in the step S2-1 into five section sections of section _1: 0-0.3; section _2: 0.3-1.0; section _3: 1.0-2.0; section _4: 2.0-6.0; section _5: >6.0, and setting the filtering factor and the noise threshold value as: section _1:40, 0.02; section _2:20, 0.03; section _3:30, 0.04; section _4:50, 0.05; section _5:100, 0.0001.
Further, in step S2-3, the slope of the low-frequency excitation signal is calculated using 20-point data, and the calculation formula is Slop ═ (data20-data 0)/20.
Further, the standard of the normal fluctuation signal and the impulse noise signal in the step S2-4 is: and when the slope is sequentially increased or decreased for not less than 8 times, judging as a normal fluctuation signal, only performing fitting smoothing processing based on model data and outputting, and otherwise, judging and identifying the pulse noise signal.
Specifically, the criteria of the impulse noise signal are: and when the number of data with the numerical difference of the adjacent low-frequency excitation signals larger than 5 times of the noise threshold is not more than 3 and the number of the noise slope values larger than the ratio of the noise threshold to the filter factor is not more than 3, determining that the low-frequency excitation signals are pulse noise signals, and performing filtering processing based on mathematical morphology and fitting smoothing processing based on discrete data and outputting.
Specifically, the basic operations based on the filtering processing of mathematical morphology include decaying candle division, Dilation, Opening operation and Closing operation, the input signal is f (n), the structural elements are g (n),
(n) the mathematical formula for the corrosion operation of g (n) is as follows:
in the formula, the symbol Θ represents the corrosion operation. In morphological transformation, the structuring element g (n) corresponds to the filtering window in the signal processing. The erosion operation eliminates the negative pulses of the signal.
(n) the mathematical formula for the expansion operation of g (n) is as follows:
in the formula, symbol
Indicating the dilation operation. In morphological transformation, the dilation operation eliminates the positive pulse of the signal.
f (n) the mathematical formulas of the form opening and closing operations of g (n) are respectively:
in the formula, symbols omicron and · denote an on operation and an off operation, respectively. The opening operation is to adopt structural elements of the same type and size to carry out candle erosion and expansion on a signal, so as to eliminate details and isolated points in a target signal and narrow 'burrs' superposed on the signal, so that the profile of the target signal is smooth, and therefore, a peak is removed, and positive pulse (peak value) noise is suppressed; the closed operation is to expand the signal first and then decay the signal, in order to fill up small holes and narrow 'cracks' in the target signal, filter the valley noise, thereby compensate the valley bottom and suppress the negative pulse (valley) noise.
In the algorithm, the mathematical form filtering adopts a classical form Open-Close (Open-Closing) filter and a Close-Open filter (Close-Opening) filter to secondarily remove positive and negative pulse interference in a target signal.
OC(f(n))＝fοg·g
CO(f(n))＝f·gοg
In the formula, OC denotes a form-on/off filter, and CO denotes a form-off/on filter.
In order to effectively filter various noise interferences, an average combined morphological filter with morphological open/close and morphological close/open is constructed by morphological open/close operation cascade as an output:
in the formula, y (n) represents the output result of the average combination shape filter. The reconstructed signal is defined as:
γ(n)＝f(n)-y(n)
specifically, the fitting smoothing process based on the discrete data is performed by using a polynomial fitting algorithm.
Specifically, in the fitting smoothing processing based on the model data, whether the signal to be output fluctuates or not and whether the curve type of the signal to be output is linear or not are judged according to a noise threshold, if the curve type is linear, the unfiltered signal is linearly output, and the filtered signal is subjected to filtering compensation calculation according to the type of a noise interval; if the noise interval is not linear, the noise interval is judged to be a curve, linear output is adopted when the filtering threshold is not more than 20 times, and otherwise, filtering compensation calculation is carried out according to the noise interval.
Specifically, a slope of 10-point data at a previous time (Slop0 ═ data9-data0)/9) and a slope of 10-point data to be output (Slop1 ═ data10-data0)/10, Slop2 ═ data11-data1)/10, … Slop10 ═ data19-data9)/10 are calculated, whether a 10-point signal fluctuates or not is judged according to a noise threshold, whether a 10-point signal curve type is linear or not is linear, if the 10-point signal curve type is linear, unfiltered signals are linearly output, and filtered signals are subjected to filtering compensation calculation according to a noise interval type. If the data is not linear type, judging the data is a curve type, predicting the data2 points before the next moment by adopting a cubic exponential smoothing algorithm to the data of 10 points at the last moment, subtracting the predicted second point data from the data of 10 points to be filtered, and adopting linear output when the data is not more than 20 times of a filtering threshold value, otherwise, carrying out filtering compensation calculation according to the type of a noise interval.
While embodiments of the present invention have been described with reference to flow diagrams, the present invention is not limited to the above-described embodiments, which are intended to be illustrative rather than limiting, and many modifications may be made by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An algorithm for detecting and processing a noise signal of a low-frequency excitation electromagnetic flowmeter is characterized by comprising the following steps of:
s1: collecting and storing signals of the low-frequency excitation electromagnetic flowmeter;
s2: processing signals of the low-frequency excitation electromagnetic flowmeter;
s2-1: calculating the standard deviation of the low-frequency excitation signal;
s2-2: setting a noise threshold value and a filtering factor according to the standard deviation calculated by the S2-1;
s2-3: calculating the slope of the low-frequency excitation signal;
and S2-4, classifying the signals of the low-frequency excitation electromagnetic flowmeter according to the noise threshold, the filtering factor and the low-frequency excitation signal slope obtained in S2-2 and S2-3, and setting the standard of normal fluctuation signals and pulse noise signals.
S3: carrying out noise reduction analysis on the low-frequency excitation noise signal;
s3-1: judging whether the low-frequency excitation electromagnetic flowmeter signal is a normal fluctuation signal or not according to the classification in 2-4, and if so, performing fitting smoothing processing based on model data;
s3-2: in S3-1, if the determination result is negative, it is continuously determined whether the signal is an impulse noise signal, and if not, fitting smoothing processing based on the model data is performed;
s3-3: in S3-2, if the judgment result is yes, filtering is carried out;
s3-4: filtering processing based on mathematical morphology is carried out;
s3-5: the low-frequency excitation flowmeter signal based on the mathematical morphology filtering processing at S3-4 continues to be subjected to fitting smoothing processing based on discrete data;
s3-6: and performing fitting smoothing based on model data on the low-frequency excitation flow meter signal on the basis of S3-5.
2. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 1, is characterized in that: the method for calculating the Standard deviation of the low-frequency excitation signal in step S2-1 includes calculating the Standard deviation once for each low-frequency excitation signal acquired at 10 data points, calculating a Standard deviation value for each three point, obtaining 8 Standard deviation values for 10 data points, and summing the 8 Standard deviation values to obtain the Standard deviation value of 10 data points, where the Standard deviation values are Standard _ error _1(1,2,3), Standard _ error _2(2,3,4).
3. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 2 is characterized in that: the noise threshold value and the filter factor in the step S2-2 are set according to the following criteria,
quantizing the standard deviation value obtained in the step S2-1 into five section sections of section _1: 0-0.3; section _2: 0.3-1.0; section _3: 1.0-2.0; section _4: 2.0-6.0; section _5: >6.0, and setting the filtering factor and the noise threshold value as: section _1:40, 0.02; section _2:20, 0.03; section _3:30, 0.04; section _4:50, 0.05; section _5:100, 0.0001.
4. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 3, characterized in that: in step S2-3, the slope of the low-frequency excitation signal is calculated using 20 points of data, and the calculation formula is Slop ═ (data20-data 0)/20.
5. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 4, wherein the standard of the normal fluctuation signal and the pulse noise signal in the step S2-4 is as follows: and when the slope is sequentially increased or decreased for not less than 8 times, judging as a normal fluctuation signal, otherwise, judging and identifying the pulse noise signal.
6. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 4, wherein the standard of the pulse noise signal is as follows: and when the number of data with the numerical difference of the adjacent low-frequency excitation signals larger than 5 times of the noise threshold is not larger than 3 and the number of the noise slope values larger than the ratio of the noise threshold to the filter factor is not larger than 3, determining that the pulse noise signals are generated.
7. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to any one of claims 1 to 6, characterized in that: the basic operations based on the mathematical morphology filtering processing comprise an Erosion candle division, an expansion Erosis, an Opening operation and a Closing operation, the input signal is f (n), the structural elements are g (n),
(n) the mathematical formula for the corrosion operation of g (n) is as follows:
(n) the mathematical formula for the expansion operation of g (n) is as follows:
f (n) the mathematical formulas of the form opening and closing operations of g (n) are respectively:
8. the algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 7, is characterized in that: and the fitting smoothing processing based on the discrete data is carried out by adopting a polynomial fitting algorithm.
9. The algorithm for detecting and processing the noise signal of the low-frequency excitation electromagnetic flow meter according to claim 8, is characterized in that: in the model data fitting smoothing processing, whether the signal to be output fluctuates or not and whether the curve type of the signal to be output is linear or not are judged according to a noise threshold value, if the curve type of the signal to be output is linear, the unfiltered signal is linearly output, and the filtered signal is subjected to filtering compensation calculation according to the type of a noise interval; if the noise interval is not linear, the noise interval is judged to be a curve, linear output is adopted when the filtering threshold is not more than 20 times, and otherwise, filtering compensation calculation is carried out according to the noise interval.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010516882.2A CN111780817B (en) | 2020-06-07 | 2020-06-07 | Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010516882.2A CN111780817B (en) | 2020-06-07 | 2020-06-07 | Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111780817A true CN111780817A (en) | 2020-10-16 |
| CN111780817B CN111780817B (en) | 2022-02-11 |
Family
ID=72753622
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010516882.2A Active CN111780817B (en) | 2020-06-07 | 2020-06-07 | Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111780817B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117033911A (en) * | 2023-10-07 | 2023-11-10 | 深圳市魔样科技有限公司 | Step counting analysis method based on intelligent glasses data |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030213282A1 (en) * | 2002-05-14 | 2003-11-20 | Helmut Brockhaus | Method for determining the uncertainty factor of a measuring procedure employing a measuring frequency |
| US6718302B1 (en) * | 1997-10-20 | 2004-04-06 | Sony Corporation | Method for utilizing validity constraints in a speech endpoint detector |
| CN1710445A (en) * | 2005-07-05 | 2005-12-21 | 华东师范大学 | Aeronautical high-spectrum remote-sensing image spectrum zonc noise self-detecting and removing method |
| CN1818690A (en) * | 2006-02-06 | 2006-08-16 | 中国矿业大学 | Conducting noise controller and controlling method for electromagnetic compatible device |
| CN104793253A (en) * | 2015-04-22 | 2015-07-22 | 吉林大学 | Airborne electromagnetic data denoising method based on mathematical morphology |
| CN105652325A (en) * | 2016-01-05 | 2016-06-08 | 吉林大学 | Exponential fit-adaptive Kalman-based ground-air electromagnetic data de-noising method |
| CN106154344A (en) * | 2016-08-01 | 2016-11-23 | 湖南文理学院 | A kind of Magnetotelluric signal denoising method based on combined filter |
| CN107478280A (en) * | 2017-08-17 | 2017-12-15 | 合肥工业大学 | A kind of water-coal-slurry electromagnetic flowmeter signal processing method based on the analysis of excitation frequency higher hamonic wave |
| CN109101910A (en) * | 2018-07-31 | 2018-12-28 | 湖南师范大学 | A kind of Magnetotelluric signal denoising method screened based on noise |
| CN209821150U (en) * | 2018-09-06 | 2019-12-20 | 微动公司 | Magnetic flowmeter and process transmitter |
| CN110989020A (en) * | 2019-12-12 | 2020-04-10 | 核工业北京地质研究院 | Filtering method and system for noise interference of audio magnetotelluric data |
-
2020
- 2020-06-07 CN CN202010516882.2A patent/CN111780817B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6718302B1 (en) * | 1997-10-20 | 2004-04-06 | Sony Corporation | Method for utilizing validity constraints in a speech endpoint detector |
| US20030213282A1 (en) * | 2002-05-14 | 2003-11-20 | Helmut Brockhaus | Method for determining the uncertainty factor of a measuring procedure employing a measuring frequency |
| CN1710445A (en) * | 2005-07-05 | 2005-12-21 | 华东师范大学 | Aeronautical high-spectrum remote-sensing image spectrum zonc noise self-detecting and removing method |
| CN1818690A (en) * | 2006-02-06 | 2006-08-16 | 中国矿业大学 | Conducting noise controller and controlling method for electromagnetic compatible device |
| CN104793253A (en) * | 2015-04-22 | 2015-07-22 | 吉林大学 | Airborne electromagnetic data denoising method based on mathematical morphology |
| CN105652325A (en) * | 2016-01-05 | 2016-06-08 | 吉林大学 | Exponential fit-adaptive Kalman-based ground-air electromagnetic data de-noising method |
| CN106154344A (en) * | 2016-08-01 | 2016-11-23 | 湖南文理学院 | A kind of Magnetotelluric signal denoising method based on combined filter |
| CN107478280A (en) * | 2017-08-17 | 2017-12-15 | 合肥工业大学 | A kind of water-coal-slurry electromagnetic flowmeter signal processing method based on the analysis of excitation frequency higher hamonic wave |
| CN109101910A (en) * | 2018-07-31 | 2018-12-28 | 湖南师范大学 | A kind of Magnetotelluric signal denoising method screened based on noise |
| CN209821150U (en) * | 2018-09-06 | 2019-12-20 | 微动公司 | Magnetic flowmeter and process transmitter |
| CN110989020A (en) * | 2019-12-12 | 2020-04-10 | 核工业北京地质研究院 | Filtering method and system for noise interference of audio magnetotelluric data |
Non-Patent Citations (7)
| Title |
|---|
| LI JIN 等: "SIGNAL-NOISE IDENTIFICATION OF MAGNETOTELLURIC SIGNALS USING FRACTAL-ENTROPY AND CLUSTERING ALGORITHM FOR TARGETED DE-NOISING", 《FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY》 * |
| 卿光明: "电磁流量计检测技术的研究——流量信号获取与处理", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 李晋 等: "基于信噪辨识的矿集区大地电磁噪声压制", 《地球物理学报》 * |
| 李晋 等: "基于递归分析和聚类的大地电磁信噪辨识及分离", 《地球物理学报》 * |
| 杨生: "大地电磁测深法环境噪声抑制研究及其应用", 《中国优秀博硕士学位论文全文数据库(博士) 基础科学辑》 * |
| 梁利平: "电磁流量传感器浆液流量信号处理方法研究与实现", 《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅱ辑》 * |
| 汤井田 等: "基于数学形态滤波的大地电磁强干扰分离方法", 《中南大学学报(自然科学版)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117033911A (en) * | 2023-10-07 | 2023-11-10 | 深圳市魔样科技有限公司 | Step counting analysis method based on intelligent glasses data |
| CN117033911B (en) * | 2023-10-07 | 2024-01-30 | 深圳市魔样科技有限公司 | Step counting analysis method based on intelligent glasses data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111780817B (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111444168A (en) | Distribution room transformer daily maximum load abnormal data detection processing method | |
| CN109492708B (en) | LS-KNN-based pipeline magnetic flux leakage internal detection missing data interpolation method | |
| CN111780817B (en) | Algorithm for detecting and processing noise signal of low-frequency excitation electromagnetic flowmeter | |
| CN114818363B (en) | Urban deep drainage tunnel siltation risk assessment and prediction early warning method | |
| CN116933044B (en) | Intelligent processing method and system for power supply data | |
| CN113095694B (en) | Rainfall sand transportation model construction method suitable for multiple landform type areas | |
| CN111241478A (en) | Flood frequency estimation method considering historical flood | |
| Chorna et al. | Identification of changes in the parameters of induction motors during monitoring by measuring the induction of a magnetic field on the stator surface | |
| Özger et al. | Sediment load prediction by combined fuzzy logic-wavelet method | |
| Yevjevich et al. | Properties of non-homogeneous hydrologic time series | |
| CN114460527A (en) | Correlation degree continuation Hibert phase-shift electronic transformer calibrator source tracing method and system | |
| CN113094934B (en) | Pipeline corrosion depth prediction method driven by physical model and data fusion | |
| CN113887119B (en) | River water quality prediction method based on SARIMA-LSTM | |
| CN113887033B (en) | Tidal river reach sediment accumulation prediction method | |
| CN109326332B (en) | Tidal water network area water environment capacity calculation method | |
| CN108427654B (en) | Rapid calculation method for silted storage capacity of medium-sized or over-sized check dam | |
| CN115688865A (en) | Industrial soft measurement method for long and short term memory network for flue gas of desulfurization process | |
| CN115900898A (en) | Signal model establishment and measurement accuracy evaluation of permanent magnet sodium flowmeter with embedded vortex generator | |
| Liang et al. | SWT based separation method for periodic signal with non-stationary noise and its application in EMF | |
| Li et al. | Signal processing scheme of step excitation electromagnetic flowmeter based on outlier elimination and double median filtering | |
| CN113009545A (en) | Computing method for net counting of seawater radionuclides | |
| Ke et al. | Real-time data mining in magnetic flux leakage detecting in boiler pipeline | |
| Ren et al. | Research on noise reduction method of pressure pulsation signal of draft tube of hydropower unit based on ALIF-SVD | |
| Das et al. | Data driven approach for performance assessment of linear and nonlinear Kalman filters | |
| CN117520752B (en) | Hydraulic engineering information management method based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220524 Address after: 067000 yujiaying Industrial Park, Shangbancheng, Chengde City, Hebei Province Patentee after: Zhongwei Electronic Co.,Ltd. Chengde City Address before: 067000 No.2 Xueyuan Road, Chengde hi tech Industrial Development Zone, Chengde City, Hebei Province Patentee before: Hebei Petroleum University of Technology |